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Citation: Vásconez, F.; Moussallam,
Y.; Harris, A.J.L.; Latchimy, T.;
Kelfoun, K.; Bontemps, M.; Macías,
C.; Hidalgo, S.; Córdova, J.; Battaglia,
J.; et al. VIGIA: A Thermal and
Visible Imagery System to Track
Volcanic Explosions. Remote Sens.
2022,14, 3355. https://doi.org/
10.3390/rs14143355
Academic Editor: Gaetana Ganci
Received: 27 May 2022
Accepted: 7 July 2022
Published: 12 July 2022
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remote sensing
Article
VIGIA: A Thermal and Visible Imagery System to Track
Volcanic Explosions
Freddy Vásconez 1, 2, * , Yves Moussallam 3, Andrew J. L. Harris 1, Thierry Latchimy 1, Karim Kelfoun 1,
Martial Bontemps 1, Carlos Macías 2, Silvana Hidalgo 2, Jorge Córdova 4, Jean Battaglia 1, Jessica Mejía2,
Santiago Arrais 2, Luis Vélez 2and Cristina Ramos 2
1Laboratoire Magmas et Volcans, CNRS, IRD, OPGC, UniversitéClermont Auvergne,
63000 Clermont-Ferrand, France; andrew.harris@uca.fr (A.J.L.H.); t.latchimy@opgc.fr (T.L.);
karim.kelfoun@uca.fr (K.K.); martial.bontemps@uca.fr (M.B.); jean.battaglia@uca.fr (J.B.)
2Instituto Geofísico, Escuela Politécnica Nacional, Ap. 17–01–2759, Quito 170525, Ecuador;
cmacias@igepn.edu.ec (C.M.); shidalgo@igepn.edu.ec (S.H.); jmejia@igepn.edu.ec (J.M.);
sarrais@igepn.edu.ec (S.A.); lvelez@igepn.edu.ec (L.V.); cramos@igepn.edu.ec (C.R.)
3Lamont-Doherty Earth Observatory, Columbia University, New York, NY 10964, USA;
yves.moussallam@ldeo.columbia.edu
4Division of Control Systems LRS, Technische Universität Kaiserslautern, 67663 Kaiserslautern, Germany;
cordova@rhrk.uni-kl.de
*Correspondence: freddy.vasconez@uca.fr or asconez@igepn.edu.ec
Abstract:
The monitoring of the frequency, intensity/magnitude and dynamics of explosive events at
volcanoes in a state of unrest is key to surveying and forecasting their activity. Thermal and visual
video observations of eruptive phenomena, and their correlation with data from deformation and
seismic networks, are often limited by technical constraints including lack of time synchronisation,
data volumes and power consumption. Several solutions are currently available and here we present
an instrument designed for the permanent and real-time observation of volcanic explosive events in
the visible and thermal infrared wavelengths, the output of which can be fully synchronised with
ancillary monitoring data. Our system (VIGIA: visual and infrared ground-based imagery analyser)
follows an edge computing approach whereby information is processed on-site, and periodic reports
are sent to the local observatory and the system “decides” when to acquire high-temporal resolution
data so as to capture key explosive events. As a permanent installation, VIGIA enables the continuous,
long-term and time-synchronised observation of volcanic activity while reducing power consumption
and data volumes. We suggest that VIGIA-style instruments could provide useful scientific and
monitoring information, and provide here the key details of the components, assembly, and code so
that observatories can replicate the system and build their own VIGIA at minimal cost. We use the
Reventador volcano, in Ecuador, as a case study to present the capabilities of the instrument.
Keywords: volcanic explosions; thermal image; video surveillance; pattern recognition
1. Introduction
Explosive volcanic eruptions can have damaging consequences over a wide range
of scales, from the global scale, as seen in the 1991 eruption of Pinatubo in the Philip-
pines (e.g., [
1
]); the regional scale such as the 2010 eruption of Eyjafjallajökull in Iceland
(e.g., [
2
]); and the local scale such as the 1999–2017 Tungurahua and 2002 Reventador
eruptions (e.g., [
3
,
4
]). The dynamics of volcanic explosions have been studied from two
different perspectives: to constrain the parameters of the eruptive regime (e.g., [
5
–
9
]) and
to model the dispersion of pyroclasts (e.g., [
10
–
12
]). Describing the dynamics of volcanic
explosive eruptions at the local level is key for the volcanic observatories’ aim of reducing
the associated risk. Video cameras have been used for several decades to monitor and
study explosive eruptions (e.g., [
7
,
13
]); more recently, the use of infrared thermal imag-
ing has been added as remote and ground-based surveillance (e.g., [
14
]). Airborne- and
Remote Sens. 2022,14, 3355. https://doi.org/10.3390/rs14143355 https://www.mdpi.com/journal/remotesensing
Remote Sens. 2022,14, 3355 2 of 17
ground-based thermal infrared imagery has proved to be an efficient and powerful tool to
monitor volcanic activity. It has been used to observe superficial changes around volcanic
craters (e.g., [
15
–
17
]), locate and track lava flows (e.g., [
18
–
20
]), identify pyroclastic density
current deposits (e.g., [
21
,
22
]), estimate lava effusion rates (e.g., [
23
,
24
]), study lava domes’
growth and collapse [
25
], monitor lava lakes (e.g., [
26
,
27
]), characterise explosive eruptions
(e.g., [
28
,
29
]) and describe plume shape and dynamics (e.g., [
30
–
32
]). Most of these results
have been obtained during measurement campaigns, either with fixed instruments or
mounted on manned or autonomous aircraft (including unmanned aerial vehicles, UAVs).
Thermal and visual video observations of eruptive phenomena, and their correlation with
data from deformation, acoustic and seismic networks, however, are often limited by
technical constraints. Limitations include time synchronisation, data volumes and power
consumption. Although these constraints can all be addressed (e.g., [
33
,
34
]), there are still
few observatories that use permanent stations for long-term studies.
In this work, we present VIGIA, an instrument based on thermal and visible imagery
intended to serve as a permanent observation station that follows an edge computing
approach whereby information is processed on site. We use the Reventador volcano in
Ecuador as a case study to prove the capabilities of the instrument, which decides when to
acquire high-temporal resolution data so as to capture key explosive eruptions and sends
periodic reports to the Instituto Geofísico of the Escuela Politécnica Nacional (IGEPN), the
local observatory.
The Reventador volcano is a stratovolcano located in the limit between the Andean
cordillera and the Amazon basin (Figure 1a). Its currently active edifice is located inside a
4 km wide U-shaped debris avalanche scar open to the east (Figure 1b). Its last, and still
ongoing, activity period started in November 2002 with almost no precursory activity; the
paroxysmal phase of that eruption started with a VEI-4 explosion that produced an ash
column 16–17 km high, and pyroclastic currents which flowed east, and reached the Coca
River, 8 km to the east [3].
Figure 1.
(
a
) Reventador volcano is located in the limit between the Andean cordillera and the
Amazon basin in Ecuador. (
b
) The current active cone is located inside a 4 km wide U-shaped debris
avalanche scar. VIGIA is installed within the property of Hostería Reventador, 7 km eastward from
the active cone.
2. Brief, Non-Exhaustive, History of Permanently Deployed Instrumentation
for Thermography
One of the earliest remote temperature measurements in the study of volcanoes
(see Figure 2) was documented by Thomas A. Jaggar in his investigation at Kilauea [
35
].
Although Jaggar used Seger cones, commonly employed in pottery, to determine the tem-
perature gradient in the uppermost 15 m of the Halema’uma’u lava lake [
36
], he also
mentioned that Daly, in 1909, and Day and Shepherd, in 1912, used optical pyrometers to
measure the temperature of active lava fountains in the same lava lake. Several authors
Remote Sens. 2022,14, 3355 3 of 17
followed this schema and carried out measurement campaigns to obtain the volcano-
feature’s surface temperature from several kilometres away; for example, the temperature
of the dacitic dome’s surface at Santiaguito volcano was measured by Zies (1941) [
37
],
who used an optical pyrometer calibrated in the range of 550 to 950
◦
C to obtain lava
temperatures of 700
±
25
◦
C. It was in 1967, that Zettwoog and Tazieff promoted the use
of permanently installed instrumentation, deployed at Etna, to measure vent tempera-
tures [
38
]. In the same context, in his 1971 report for UNESCO [
39
], Robert M. Moxham
presented the state-of-the-art possibilities for permanent deployments, which included
portable direct-reading radiometers with optional recorders, and aerial radiometric systems.
The main challenges at that time were the harsh environmental conditions and equipment
bulk [
40
].
Moxham et al. (1972) [41]
conducted an experiment in which temperature data
from fumaroles at Mount Rainier were received in Washington via a satellite link. In this
experiment, an insulated plywood box contained the instruments including the main power
supply battery which was replenished by a solar-cell panel. The box was heated by a system
connected to a wind generator. Although some of the instruments were destroyed after five
weeks, the results were optimistically reported, confirming the feasibility of using satellites
for the telemetry of volcanic temperatures. Another attempt to implement instrumentation
to measure volcanic temperature was executed in 1978 by Brivio and Tomasoni (1980) [
42
],
who modified a commercial radiometer and installed it on a 2.5 m high pillar overlooking
the fumarolic field at Vulcano. The data were transmitted by a 5 km radio link back to
Lipari. Once again, environmental conditions, high salinity, humidity, wind, and rain
eventually caused the system to fail after a few months.
A significant step in increasing the durability of instruments deployed in the harsh
environment that often characterise volcanic landscape was achieved by Carl R. Thornber
(1997) [
43
], who successfully installed a remote video telemetry system (RVTS) for the
surveillance of Kilauea, transmitting visible images of the Pu’u O’o eruptive vent to the
Hawaiian Volcano Observatory (HVO). Despite not having pursued temperature measure-
ments, this RVTS was the basis for the development of the thermal monitoring system for
Kilauea volcano, which was named DUCKS, designed and built in 1999 by
Harris et al. [44]
.
The DUCKS system was a low-cost robust modular system based on thermal infrared
thermometers that used radio transceivers to transmit thermal data to the HVO. The system
was modified to be installed at Stromboli volcano in 2002, where its robustness was severely
tested during the paroxysmal eruption of 5 April 2003. The instrument recorded good,
unsaturated data during the whole eruption [
45
]. Based on the idea of robustness, Aster
et al. (2004) [
46
] installed an integrated surveillance instrumental network at Mount Erebus
volcano, in Antarctica, which included dual-frequency GPS receivers, broadband seis-
mometers, digital radiometers, video cameras and radio transceivers for data transmission.
The RVTS was also the starting point for the development of volcano monitoring systems
based on thermal imaging. Camcorder-style thermal cameras appeared in the market from
1995 [
47
], but it took some years until they evolved to fulfil the necessary characteristics
to function as parts of permanent deployed stations. In 2004, two thermal cameras were
added to the existing video surveillance system at Etna volcano [
48
], allowing the imple-
mentation of real-time analysis of the images to discriminate the type of activity (between
gas emission, lava effusion and absence of activity) and thus change the storing rate of
the images and provide statistics of volcanic phenomena. The deployment of this system
resulted in the detailed study of the 2011–2013 Etna lava fountaining episode (e.g., [
49
,
50
]).
In 2006, Lodato et al. (2008) [
51
] installed a thermal camera at Vulcano to monitor the
fumarolic field. Delle Donne et al. (2006) [
52
] used a permanently installed thermal camera
to automatically evaluate the volcanic explosion rate, the magnitude of the explosions,
the maximum height above the crater rim reached by the ejecta, the maximum velocity
of the pyroclastic material and gas, and the distribution of the thermal activity along the
active vents at Stromboli. Many equipment features have improved over the last few
years, allowing equipment to be interconnected, sampling rates to be increased and image
resolution to be boosted. Another example of permanent deployment based on thermal
Remote Sens. 2022,14, 3355 4 of 17
cameras for volcanic surveillance was described by Patrick et al. (2014) [
34
], who installed
thermal cameras between 2010 and 2012 at Kilauea and Mauna Loa in order to monitor
lava lakes, intracrater vents, fissure eruptions, lava flows and fumarolic activity. During the
subsequent years, image processing algorithms using visible images were implemented
to calculate plume heights [
50
,
53
] and to estimate mass eruption rates [
54
]. In 2014, a
team from Laboratoire Magmas et Volcans (LMV) and the Observatoire de Physique du
Globe de Clermont Ferrand (OPGC) implemented an integral system based on thermal
and visible imagery to monitor the lava dome dynamics at the Merapi volcano [
25
]. The
system includes four stations, placed in pairs, so that they produce stereoscopic images
that allow objects to be spatially located and their trajectories reconstructed. Each station
contains a digital single lens reflex camera (DSLR), an infrared thermal camera and one or
two webcams. The instrument presented in this paper is based on the central idea of the
instrument developed for Merapi.
Figure 2.
A brief (non-exhaustive) chronology of the evolution of permanently installed instrumen-
tation for measuring temperature at volcanoes. Each milestone is defined by a scientific publica-
tion [
25
,
34
–
36
,
38
,
39
,
41
–
44
,
46
–
48
]; the author is indicated in blue letters and the year of publication in
parentheses. The pins show the installation time, some of the above systems remain operational at
the time of writing (2022).
3. Materials and Methods
Our instrument, VIGIA (visual and infrared ground-based imagery analyser; named
in honour of the Vigia network of local observers during the Tungurahua 1999–2017
eruption; [
55
,
56
]), was developed to record and analyse thermal and visible images at a
high sampling rate (32 images per second) during volcanic explosive eruptions. The main
components of this instrument are grouped as depicted in the diagram of Figure 3a, into
four groups: (1) the power supply system; (2) the computing unit, which includes the
timing module, the external pressure–temperature–relative-humidity (P-T-RH) sensor, the
Ethernet port and the storage drive; (3) the cameras, consisting of the thermal module
and the visible module; and (4) the communications module. VIGIA was conceived to
work permanently, thus the power supply and communications were designed to keep the
instrument running continuously and to optimize the amount of data transmitted. The
computing unit, hereinafter referred to simply as the central computer, is the backbone
of the instrument, having the function of controlling the cameras, retrieving the images
and processing them. It is based on a Raspberry Pi 4 as the hardware core running
Raspberry OS (previously Raspbian) which is an operating system based on the Debian
Linux distribution. Although the instrument can operate autonomously, there is also the
possibility to control it remotely. The complete set of configuration files and scripts can be
found as Supplementary Material.
Remote Sens. 2022,14, 3355 5 of 17
Figure 3.
(
a
) Block diagram of the components of VIGIA. The physical disposition of the elements
inside the case is shown in (
b
). Most of the elements composing the power system are physically
placed out of the main case (
c
); those that remain inside are the power management board (4) and
the web relay (5), shown in panel (
d
). Panel (
e
) contains the central computer (6) and its peripheric
devices: (7) GPS module, (8) pressure–temperature–relative humidity (P-T-RH) sensor, (9) Ethernet
hub and (10) solid state drive (SSD). A zoomed view of the P-T-RH sensor is displayed in panel (
f
).
Panel (
g
) shows the disposition of the thermal camera (12), the visible camera (MIPI CSI) (13) and the
window defog heater (14).
3.1. Power System
VIGIA is powered by a backed-up 24 VDC power supply (Figure 3c) in which the
main source is the national power grid (110 VAC-60 Hz in Ecuador, Figure 3c-1) and the
backup source is a stand-alone photovoltaic power system (Figure 3c-2) consisting of two
50 W solar panels that recharge a battery bank of two 70 Ah lead-acid batteries. A diode-
based circuit (Figure 3c-3) that was designed at the Division of Control Systems LRS of
the Technische Universität Kaiserslautern and built at the IGEPN, allows the system to be
connected to the power source with the highest voltage. Figure 4depicts the schematics
of the diode-based circuit, which uses Schottky diodes with low internal resistance and
Remote Sens. 2022,14, 3355 6 of 17
very low reverse current. In case of failure in the national power network, the photovoltaic
system provides the energy necessary for the instrument to run normally and, in case the
solar panels get covered or disconnected, external lead-acid batteries provide an autonomy
of up to 72 h. The instrument’s components receive the power they need to operate from a
power management board (Figure 3d-4), designed and built at the OPGC, which ensures
that the voltage delivered to each component is stable. A web relay (Figure 3d-5) controls
individually the power of the central computer (Figure 3e-6), the DSLR camera (see Visual
Module section below) (Figure 3b-11), the window defog heater (Figure 3g-14), and includes
the possibility to send signals remotely for a soft reboot, shutdown and a hard reboot of the
central computer.
Figure 4.
This diode-based circuit allows the implementation of a dual power supply system,
switching between Vin_1 and Vin_2 depending on the higher voltage. It uses Schottky diodes with
low dynamic resistance and very low reverse current to minimize the power transfer between the
two power sources.
3.2. Computer Unit
The computer unit is responsible for data acquisition, processing and communication.
It counts with a GPS module (Figure 3e-7) to ensure that all data recorded by the VIGIA
is UTC time stamped to microsecond accuracy. The GPS module used is based on the
L80–39 chip, which uses a serial port for communication and needs a GPIO pin to send
the pulse-per-second signal. The system synchronises its own clock with the GPS time
using standard Linux utilities (gpsd, pps-tools and ntp). An external sensor (P-T-RH)
(Figure 3f-8) measures ambient pressure, ambient temperature and relative humidity to
check the possible influence of these parameters on the thermal data (script included
within the Supplementary Material). The transmission of the data processing results takes
place mainly via the Ethernet connection (Figure 3b,d-9). The communication process is
described in more detail below. The raw data, as well as the processed results are stored on
a solid-state drive (SSD) (Figure 3e-10). The SSD was chosen instead of a hard disk drive
(HDD) because of its reliability against shock, vibration and extreme temperatures, not to
mention write and read speed.
3.3. Thermal Module
We used an 8–14
µ
m Optris PI 640 thermal camera (Figure 3b,g-12) to record static
640 ×480
pixel images and 32 frames-per-second (fps) video. Evocortex provides a Soft-
ware Development Kit (SDK) called IRImagerDirect SDK [
57
] which includes the PI imager
library to control the camera and the documentation. The library can be integrated into C++
programs and also in Python scripts to connect to the camera and retrieve images including
metadata. VIGIA has different modes for recording thermal data, depending on the status
of the volcano. If the volcano is clouded, the instrument remains in a waiting state, just
Remote Sens. 2022,14, 3355 7 of 17
checking periodically whether the volcano becomes clear or not (see Volcano Recognition
section below). Once the volcano becomes unclouded, the crater is identifiable, then the
system goes into an acquisition state in which it acquires periodic static thermal pictures and,
by obtaining the maximum temperature within a region of interest (ROI) located just above
the crater rim, it generates a thermal timeseries with the maximum temperature in the ROI
at 32 samples per second (sps). The central computer applies a triggering system on the
thermal timeseries to identify explosive eruptions. Every time an explosion is detected, the
system records a thermal video.
For computers, a thermal image is an array of floating-point numbers (32 or 64 bits)
representing the temperature of each pixel, plus an array of integers containing the metadata
(time stamp, camera identifier, among other information). Optris, the thermal camera
manufacturer, uses lossless compression algorithms to change the representation to 16-bit
integers. We used the Python module Numpy to manage and store data with its conventional
.npz extension. Each thermal file has an identifier in the name telling the type of data
contained. Examples of thermal filenames are VIGIA_IR_<date>_<time>.npz for still
thermal infrared picture, VIGIA_THVID_<date>_<time>.npz for thermal infrared video
frames and VIGIA_METAD_<date>_<time>.npz for thermal video metadata.
3.4. Visible Module
The visible module of the VIGIA includes a 5-megapixel visible camera (Figure 3b,g-13)
rendering images through the MIPI Camera Serial Interface (MIPI CSI) connection provided
by the Raspberry Pi, and an 18-megapixel DSLR Canon camera (Figure 3b-11) connected
using the USB port. A python script, written by the OPGC technical team, retrieves videos
from the 5-megapixel camera when an explosion is triggered. The video recorded includes
a five-second pre-trigger period and its length is set to 1.5 min. At night, all images and
videos from the 5-megapixel camera are dark, the DSLR camera is used then in bulb mode
to capture long exposure pictures rendering the volcano visible. During the daytime, the
DSLR camera captures a series of 10 individual shots. Both visual cameras are triggered by
the thermal module.
3.5. Volcano Recognition
Most volcanoes have their summit at least occasionally obstructed from view by cloud
cover. At volcanoes such as Reventador, located in the sub-Andean region where the
Amazonian Forest environment is very humid, the periods in which the volcano is clear
from cloud cover can be as small as ten hours in a week. In order to optimize the amount
of disk space and transmission bandwidth, VIGIA counts with an algorithm to recognize
whether the volcano is clear or clouded (Figure 5). This algorithm uses the contours of
the crater in good conditions (i.e., no clouds or gas in the surroundings) as a template
that the computer searches for in each picture (Figure 5a). The algorithm also uses a
weighted mask to enhance the importance of the flanks of the volcano over the upper rim
(Figure 5b). The mask was defined based on the rapid changes of the morphology observed
at the upper rim [
58
]. We applied a normalised 2D cross-correlation algorithm included in
OpenCV API to search for the template and locate the point with the highest correlation
coefficient between the image and the template. As a result of this operation, we have
the normalised the cross-correlation coefficient and the location of the pattern in row and
column coordinates from the origin; in the particular case shown in Figure 5, from the lower
left corner. We used this coefficient to discriminate if the volcano appears in the image by
simply comparing it to a predefined threshold; if the volcano is clouded, the coefficient
is very low, and the location is reported as the origin. We tested the volcano recognition
procedure in three stages. During the first stage, we used 80 images acquired at different
times of the day to adjust the threshold value above which the image is defined as clear or
cloudy. In the second stage we used 780 images from different months in which the volcano
appears in different locations inside the frame, showing that the result of the recognition is
indifferent to changes in position caused by wind, vibrations or slight displacements of
Remote Sens. 2022,14, 3355 8 of 17
the camera. The third test was performed on a sample of 2600 images taken at different
times of the day, on different days of different seasons. Each new image obtained has a
probability of success independent of the result of the previous ones. In these tests we have
defined a hit or success as the coincidence between the label resulted from the recognition
script and the rating that a volcanologist assigned to the image.
Figure 5.
From a clear image of the upper cone (
a
), we extracted the outline of the volcano and used
it to recognize the volcano in each captured image. Panel (
b
) shows the mask used in the normed
2D cross-correlation algorithm that determines whether or not the volcano appears in each new
captured image. The colour scale in panel (
a
) represents the temperature and the grayscale in panel (
b
)
represents the weight (importance) of the template pixels. In each captured image (c,e), the volcano
recognition algorithm obtains the contours (
d
,
f
) and cross-correlates them with the masked template
(
b
). The solid-line squares show the position of the volcano found. The normalised cross-correlation
coefficient, the quality, the location of the crater as a row-column pair from the lower left corner,
and the result of the recognition are shown for an image of the Reventador volcano in August 2021
(
c
,
d
) and in October 2021 (
e
,
f
), after the camera was moved slightly downwards. The algorithm is
independent of the displacement of the cone within the image. The quality factor is calculated from
the number of features (contours) and the range of temperature inside the dashed-line square.
Remote Sens. 2022,14, 3355 9 of 17
3.6. Communication Module
The communication module of the VIGIA instrument works on the physical communi-
cation components of the central computer, i.e., on the Ethernet interface. It is through this
interface that we have access to the different components and data generated by the instru-
ment. Several communication protocols allow us to remotely control the central system,
such as SSH and VNC connections, and remote desktop applications such as AnyDesk [
59
]
or DWService [
60
]. Access to the graphical environment of the host computer is necessary
specifically to observe the images from the cameras in real time in the process of framing
and focusing during field installation.
The logic part of the communications module is in charge of delivering the results of
the measurements and processing. We created a series of scripts that allow VIGIA to deliver
periodic reports about the volcano (see Supplementary Material). Each time a thermal
image is acquired, the volcano recognition script appends to a daily log file the date and
time, the normalised cross-correlation coefficient, the image quality, the position of the
volcano summit pattern, and a “Clear” or “Clouded” label. At the end of the day, a script is
dedicated to obtaining statistical data from the log file; more specifically the portion of the
day observed, the portion of that period when the volcano was unclouded, and the time
when the volcano was last seen to be clear. These data are included in a report together
with the thermal image with the best quality factor of the day and VIGIA distributes it
using a telegram bot.
4. Results
We deployed the instrument and installed it as a permanent station at Reventador
volcano, Ecuador. All the code scripts are available at https://github.com/fvasconez/
VIGIA.git (accessed on 10 February 2022). Our instrument is located 7 km away from the
active cone, within the property of the Hostería Reventador. It has been regularly recording
data since August 2021. Figure 6show examples of still images, in infrared and visible
spectra, during the beginning (Figure 6a) and the second pulse (Figure 6b) of an explosion;
it also shows a false event triggered by a simple-threshold triggering algorithm.
In the first phase, we setup VIGIA to record still thermal images every five minutes. We
used these images to define the threshold that allows the instrument to discriminate whether
the volcano appears in the image or not. In the second phase, the trigger frequency was
increased to one shot every two minutes. During this phase, we were able to experiment
with the impact of noise on the thermal images on the recognition procedure, and to
adjust the parameters of the denoising procedure to increase the success rate of volcano
recognition. In the third phase, the image capture frequency was dynamically adjusted to
the visibility conditions, capturing one image per minute under clear conditions and one
image every five minutes under cloudy conditions.
Daily, the staff of the IGEPN receives a report with the information of the visibility of
the volcano during the day, together with the best rated picture, directly to their mobile
phones (Figure 7).
Based on the still images recorded and processed, we obtained the portion of each
day when the volcano is recognizable by the station, i.e., when cloud cover is low and
measurements are possible. Figure 8shows these daily intervals, specifying whether these
are working hours (between 07h00 and 17h00, local time) during which other observation
tasks can be performed, or are intervals during the night. Figure 8includes the uncertainty,
linked to images that were corrupted or not acquired for any reason. We found that
the volcano was unclouded 20.3–41.7% of the observed time; and during working hours,
the volcano was unclouded 2.4–23.8% of the time. In other words, clear conditions for
observation were found to be mostly at night.
Remote Sens. 2022,14, 3355 10 of 17
Figure 6.
Examples of thermal and visible images captured individually showing different instants
of explosions at the Reventador volcano. (
a
) Beginning of the explosion. (
b
) A second pulse of the
explosion; note that the different ilumination of the background plume does not mean a difference
in temperature. (
c
) Many false events triggered by non volcanic events, a bird in this case, led us to
change the triggering system from a simple threshold to a method based on thermal timeseries (see
Thermal module).
Thermal imaging depends on the radiation received by the thermal camera. The
presence of clouds surrounding the volcano or the observation point impacts directly on
the number of images captured, and consequently the number of explosions detected.
Observation techniques such as seismicity or infrasound do not encounter this problem
although they lack information about the external activity of the volcano (explosions,
passive degassing, rock falls, pyroclastic currents, etc.). Unlike VIGIA, most video- and
seismic-based monitoring systems count with a module for data processing at the receiving
end (observatory), so data processing performed on-site remains minimal. Table 1shows a
comparison between VIGIA and generic seismicity- and video-based monitoring systems.
Infrared thermal images with an adequate time stamp hence complement seismic and
acoustic observational data to provide an integral description of external manifestations of
volcanic activity as shown in Figure 9. For those explosions detected by thermal imaging,
we can retrieve more information, such as the plume height, ascent rate, the volume of
ejected material, and the size and dispersion direction of the plume [
30
,
31
,
33
,
61
]. Moving
to a shorter time scale, data recorded as thermal video allows us to analyse and characterise
individual explosions (cf. [
62
–
64
]). As an example, Figure 10 shows the comparison of the
temperature of the material erupted in one of the explosions that occurred at Reventador
volcano on 2022–01–16 with its corresponding seismic waveform recorded by the REVN
seismic station, located ~4 km from the current active vent.
Remote Sens. 2022,14, 3355 11 of 17
Figure 7.
By using a telegram bot, VIGIA sends a daily report with statistics of the day based on the
volcano recognition. The picture of the day is the one where the volcano was unclouded and the
quality factor is the highest.
Figure 8.
The portion of the day when the volcano is observable by thermal camera (day and night) is
represented by stacked blue bars, while the “working” portion of the day (07h00 to 17h00, local time)
when the volcano is observable is represented with orange bars. The uncertainty, in grey, depends on
the quantity of images that are corrupted or the instrument did not acquire for any reason.
Remote Sens. 2022,14, 3355 12 of 17
Table 1. Comparison between VIGIA and generic seismicity- and video-based monitoring systems.
VIGIA System Seismicity-Based
Monitoring System
Video-Based Monitoring
System
Day/night detection Yes Yes Daytime only
Dependence on
meteorological conditions Yes No Yes
Installation complexity Medium High Medium
On-site data processing High Medium Medium
Data volume (per hour)
~20 GB (depends on the
number of explosions
detected)
~9 MB. Including
3 components, 100 sps,
metadata.
~4 GB (depends on the
resolution and compression)
Figure 9.
Infrared thermal still images obtained by VIGIA provide visible information of the state
of the volcano over time. The lower panel contains an extract of the seismic record from the REVN
seismic station located ~4 km from the current active vent of the Reventador volcano. This example
from 2022–01–17 shows thermal images with a precise time stamp that complement the information
obtained from the seismic record to describe the surficial activity of the volcano.
One second of thermal video file contains, in the case of VIGIA, 32 images of
640 ×480
pixels, which use 18.75 MB of disk space. This means that one hour of continuous thermal
video takes up about 66 GB, and one day will use 1582 GB (1.5 TB). Continuous acquisition
of high-resolution thermal video therefore makes little practical sense, especially consid-
ering periods where the view of the volcano is impeded by clouds, which, in the case of
our study, was about 60% of the time. We therefore configured the system to trigger the
recording of thermal videos only when an explosion is detected. The videos are 49 s in
duration (four seconds before the trigger plus 45 s after it), which uses around one GB of
storage space. During an average day, in terms of volcanic activity and cloud conditions,
the system identified 46 explosions, recording 41 GB of thermal video data.
Remote Sens. 2022,14, 3355 13 of 17
Figure 10.
Thermal data recorded as video allow the analysis of explosions on a short timescale. In
this example from the Reventador volcano, frames (
a
) extracted from the thermal video show the
surficial state of the volcano: (
a1
) before the plume appears over the crater; (
a2
) at the moment when
the temperature measured was maximal; (
a3
) at the middle of the explosion; and (
a4
) at the end of
the explosion. Panel (
b
) show the timeseries of the maximal temperature inside the region of interest
located just above the crater rim (ROI at (
a1
)), which can be compared with the record from the REVN
seismic station located ~4 km from the active vent, ploted in panel (c).
5. Summary and Further Work
In the current state (to June 2022), the VIGIA system captures still images in infrared
and visible spectrum, which can be used to perform long term studies, for example,
studies of morphology changes (e.g., [
65
]) and of effusion of lava flows (e.g., [
23
]). The
system automatically recognizes when the volcano is unclouded and when an explosion
occurs, then it captures high-temporal resolution thermal videos of the explosion; the
resulting videos constitute the input material for studies of explosion or plume dynamics
(e.g., [
66
,
67
]). VIGIA also records information from temperature, pressure and relative
humidity sensors. Daily, it chooses the image of the day and attaches it to the daily report
to send it to the IGEPN.
The next stage includes the capability to automatically report dynamic parameters
of the explosions, such as maximum plume height and ascent rate. The data provided by
VIGIA are already being used as input for the analysis of the long-term explosive activity
of the Reventador volcano, which is beyond the scope of this article.
6. Conclusions
We have presented VIGIA, a visible and thermal infrared observation system for active
volcanoes that follows an edge computing approach whereby the station decides when to
acquire high frequency data and transmits statistics and daily reports to the observatory
while keeping energy consumption and data volume to a minimum. We have presented
the results from the permanent deployment of such a station at the Reventador volcano
showing that it successfully identifies adequate observational conditions and automatically
triggers the recording of explosions at high resolution; explosions of which the nature
and magnitude can be directly compared to seismic information. We have described the
Remote Sens. 2022,14, 3355 14 of 17
hardware and software components of the system extensively (all code scripts available at
https://github.com/fvasconez/VIGIA.git (accessed on 10 February 2022)) in the hope that
the system can be reproduced, used, and in time, improved by other volcano observatories.
An increased and more permanent monitoring of surficial activity at active volcanoes,
possible with such systems, should enhance understanding of the volcanic processes at
work, as well as forecasting capacities.
Supplementary Materials:
The following supporting information will be immediately available at:
https://github.com/fvasconez/VIGIA.git upon publication of the manuscript: configuration files,
python scripts, shell scripts.
Author Contributions:
Conceptualization, F.V., Y.M., A.J.L.H., T.L., K.K., M.B., S.H. and J.B.; data
curation, F.V.; formal analysis, F.V.; funding acquisition, Y.M.; methodology, F.V., T.L., M.B., C.M.
and J.C.; project administration, Y.M.; resources, Y.M.; software, F.V. and T.L.; supervision, Y.M. and
A.J.L.H.; validation, F.V., Y.M., C.M., J.M., S.A., L.V. and C.R.; visualisation, F.V.; writing—original
draft, F.V. and Y.M.; writing—review and editing, F.V., Y.M., A.J.L.H., T.L., K.K., M.B., C.M., S.H.,
J.C., J.B., J.M., S.A., L.V. and C.R. All authors have read and agreed to the published version of
the manuscript.
Funding:
This research was funded by Region Auvergne Rhone Alpes through its call for projects
Pack Ambition Recherche 2018 (project OROVOLC, PI: Y.M.).
Data Availability Statement: Not applicable.
Acknowledgments:
We thank the Institut de Recherche pour le Développement, France (IRD,
Ecuador office), especially Jean-Luc Le Pennec and Pablo Samaniego for their collaboration and
support for the installation of the instruments. We would also like to express our gratitude for the
openness and collaboration of Joselito Amaguay and Rosa Alulema, who kindly opened the doors
of the Hostería Reventador to us. F.V. acknowledges support from the Region Auvergne Rhone
Alpes through its call for projects Pack Ambition Recherche 2018 (project OROVOLC, PI: Y.M.). We
are grateful for the constructive suggestions and comments of our reviewers, which undoubtedly
improved the quality of the paper. This work is part of an Ecuadorian-French cooperation programme
carried out between the IGEPN and the IRD through a “Laboratoire Mixte International” program
entitled “Séismes et Volcans dans les Andes du Nord”. This is Laboratory of excellence ClerVolc
contribution number 554.
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or
in the decision to publish the results.
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